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cs.LG2025
Subgraph Federated Learning for Local Generalization
Sungwon Kim, Yoonho Lee, Yunhak Oh +6
Federated Learning (FL) on graphs enables collaborative model training to enhance performance without compromising the privacy of each client. However, existing methods often overl…
cs.LG2025
Node-level Contrastive Unlearning on Graph Neural Networks
Hong kyu Lee, Qiuchen Zhang, Carl Yang +1
Graph unlearning aims to remove a subset of graph entities (i.e. nodes and edges) from a graph neural network (GNN) trained on the graph. Unlike machine unlearning for models train…
cs.LG2025
FedGrAINS: Personalized SubGraph Federated Learning with Adaptive Neighbor Sampling
Emir Ceyani, Han Xie, Baturalp Buyukates +2
Graphs are crucial for modeling relational and biological data. As datasets grow larger in real-world scenarios, the risk of exposing sensitive information increases, making privac…